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Background  The fungal pathogen  Fusarium graminearum  (telomorph  Gibberella zeae ) is the causal agent of several destructive crop diseases, where a set of genes usually work in concert to cause diseases to crops. To function appropriately, the  F. graminearum  proteins inside one cell should be assigned to different compartments, i.e. subcellular localizations. Therefore, the subcellular localizations of  F. graminearum  proteins can provide insights into protein functions and pathogenic mechanisms of this destructive pathogen fungus. Unfortunately, there are no subcellular localization information for  F. graminearum  proteins available now. Computational approaches provide an alternative way to predicting  F. graminearum  protein subcellular localizations due to the expensive and time-consuming biological experiments in lab.    Results  In this paper, we developed a novel predictor, namely FGsub, to predict  F. graminearum  protein subcellular localizations from the primary structures. First, a non-redundant fungi data set with subcellular localization annotation is collected from UniProtKB database and used as training set, where the subcellular locations are classified into 10 groups. Subsequently, Support Vector Machine (SVM) is trained on the training set and used to predict  F. graminearum  protein subcellular localizations for those proteins that do not have significant sequence similarity to those in training set. The performance of SVMs on training set with 10-fold cross-validation demonstrates the efficiency and effectiveness of the proposed method. In addition, for  F. graminearum  proteins that have significant sequence similarity to those in training set, BLAST is utilized to transfer annotations of homologous proteins to uncharacterized  F. graminearum  proteins so that the  F. graminearum  proteins are annotated more comprehensively.    Conclusions  In this work, we present FGsub to predict  F. graminearum  protein subcellular localizations in a comprehensive manner. We make four fold contributions to this filed. First, we present a new algorithm to cope with imbalance problem that arises in protein subcellular localization prediction, which can solve imbalance problem and avoid false positive results. Second, we design an ensemble classifier which employs feature selection to further improve prediction accuracy. Third, we use BLAST to complement machine learning based methods, which enlarges our prediction coverage. Last and most important, we predict the subcellular localizations of 12786  F. graminearum  proteins, which provide insights into protein functions and pathogenic mechanisms of this destructive pathogen fungus.

FGsub: Fusarium graminearum protein subcellular localizations predicted from primary structures